




from collections import OrderedDict
from typing import Any, List, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging


__a							     =					logging.get_logger(__name__)

__a							     =					{
    'EleutherAI/gpt-j-6B': 'https://huggingface.co/EleutherAI/gpt-j-6B/resolve/main/config.json',
    # See all GPT-J models at https://huggingface.co/models?filter=gpt_j
}

class     __a(	_a  ):






    """simple docstring"""





    lowerCAmelCase         = '''gptj'''
    lowerCAmelCase         = {
        '''max_position_embeddings''': '''n_positions''',
        '''hidden_size''': '''n_embd''',
        '''num_attention_heads''': '''n_head''',
        '''num_hidden_layers''': '''n_layer''',
    }





    def __init__( self  ,_SCREAMING_SNAKE_CASE=50_400  ,_SCREAMING_SNAKE_CASE=2_048  ,_SCREAMING_SNAKE_CASE=4_096  ,_SCREAMING_SNAKE_CASE=28  ,_SCREAMING_SNAKE_CASE=16  ,_SCREAMING_SNAKE_CASE=64  ,_SCREAMING_SNAKE_CASE=None  ,_SCREAMING_SNAKE_CASE="gelu_new"  ,_SCREAMING_SNAKE_CASE=0.0  ,_SCREAMING_SNAKE_CASE=0.0  ,_SCREAMING_SNAKE_CASE=0.0  ,_SCREAMING_SNAKE_CASE=1e-5  ,_SCREAMING_SNAKE_CASE=0.02  ,_SCREAMING_SNAKE_CASE=True  ,_SCREAMING_SNAKE_CASE=50_256  ,_SCREAMING_SNAKE_CASE=50_256  ,_SCREAMING_SNAKE_CASE=False  ,**_SCREAMING_SNAKE_CASE  ,)					->						Tuple:
         UpperCAmelCase_     :				List[str]  =     vocab_size
         UpperCAmelCase_     :				str  =     n_positions
         UpperCAmelCase_     :				Dict  =     n_embd
         UpperCAmelCase_     :				Tuple  =     n_layer
         UpperCAmelCase_     :				Any  =     n_head
         UpperCAmelCase_     :				Dict  =     n_inner
         UpperCAmelCase_     :				Dict  =     rotary_dim
         UpperCAmelCase_     :				Any  =     activation_function
         UpperCAmelCase_     :				List[Any]  =     resid_pdrop
         UpperCAmelCase_     :				Tuple  =     embd_pdrop
         UpperCAmelCase_     :				Optional[Any]  =     attn_pdrop
         UpperCAmelCase_     :				Optional[int]  =     layer_norm_epsilon
         UpperCAmelCase_     :				int  =     initializer_range
         UpperCAmelCase_     :				Dict  =     use_cache

         UpperCAmelCase_     :				Dict  =     bos_token_id
         UpperCAmelCase_     :				Union[str, Any]  =     eos_token_id

         super().__init__(
             bos_token_id=_SCREAMING_SNAKE_CASE  ,eos_token_id=_SCREAMING_SNAKE_CASE  ,tie_word_embeddings=_SCREAMING_SNAKE_CASE  ,**_SCREAMING_SNAKE_CASE    )


class     __a(	_a  ):






    """simple docstring"""





    def __init__( self  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE = "default"  ,_SCREAMING_SNAKE_CASE = None  ,_SCREAMING_SNAKE_CASE = False  ,)					->						Dict:
         super().__init__(_SCREAMING_SNAKE_CASE  ,task=_SCREAMING_SNAKE_CASE  ,patching_specs=_SCREAMING_SNAKE_CASE  ,use_past=_SCREAMING_SNAKE_CASE    )
         if not getattr(self._config  ,'''pad_token_id'''  ,_SCREAMING_SNAKE_CASE    ):
              # TODO: how to do that better?
              UpperCAmelCase_     :				Any  =     0





    @property
    def 				a__ ( self    )					->						Mapping[str, Mapping[int, str]]:
         UpperCAmelCase_     :				Optional[Any]  =     OrderedDict({'''input_ids''': {0: '''batch''', 1: '''sequence'''}}    )
         if self.use_past:
              self.fill_with_past_key_values_(_SCREAMING_SNAKE_CASE  ,direction='''inputs'''    )
              UpperCAmelCase_     :				List[Any]  =     {0: '''batch''', 1: '''past_sequence + sequence'''}
         else:
              UpperCAmelCase_     :				int  =     {0: '''batch''', 1: '''sequence'''}

         return common_inputs





    @property
    def 				a__ ( self    )					->						int:
         return self._config.n_layer





    @property
    def 				a__ ( self    )					->						int:
         return self._config.n_head





    def 				a__ ( self  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE = -1  ,_SCREAMING_SNAKE_CASE = -1  ,_SCREAMING_SNAKE_CASE = False  ,_SCREAMING_SNAKE_CASE = None  ,)					->						Mapping[str, Any]:
         UpperCAmelCase_     :				Any  =     super(_SCREAMING_SNAKE_CASE  ,self    ).generate_dummy_inputs(
             _SCREAMING_SNAKE_CASE  ,batch_size=_SCREAMING_SNAKE_CASE  ,seq_length=_SCREAMING_SNAKE_CASE  ,is_pair=_SCREAMING_SNAKE_CASE  ,framework=_SCREAMING_SNAKE_CASE    )

         # We need to order the input in the way they appears in the forward()
         UpperCAmelCase_     :				Union[str, Any]  =     OrderedDict({'''input_ids''': common_inputs['''input_ids''']}    )

         # Need to add the past_keys
         if self.use_past:
              if not is_torch_available():
                   raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.'''    )
              else:
                   import torch

                   UpperCAmelCase_,       UpperCAmelCase_     :				Any  =     common_inputs['''input_ids'''].shape
                   # Not using the same length for past_key_values
                   UpperCAmelCase_     :				int  =     seqlen + 2
                   UpperCAmelCase_     :				Optional[Any]  =     (
                       batch,
                       self.num_attention_heads,
                       past_key_values_length,
                       self._config.hidden_size // self.num_attention_heads,
                   )
                   UpperCAmelCase_     :				Optional[Any]  =     [
                       (torch.zeros(_SCREAMING_SNAKE_CASE    ), torch.zeros(_SCREAMING_SNAKE_CASE    )) for _ in range(self.num_layers    )
                   ]

         UpperCAmelCase_     :				List[Any]  =     common_inputs['''attention_mask''']
         if self.use_past:
              UpperCAmelCase_     :				List[str]  =     ordered_inputs['''attention_mask'''].dtype
              UpperCAmelCase_     :				Tuple  =     torch.cat(
                  [ordered_inputs['''attention_mask'''], torch.ones(_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,dtype=_SCREAMING_SNAKE_CASE    )]  ,dim=1    )

         return ordered_inputs





    @property
    def 				a__ ( self    )					->						int:
         return 13

"""simple docstring"""






import warnings

from ...utils import logging
from .image_processing_mobilevit import MobileViTImageProcessor


UpperCAmelCase        =				logging.get_logger(__name__)



class 			lowercase					( lowercase__		):






			def __init__(self  :	Dict      ,*SCREAMING_SNAKE_CASE_  :	str      ,**SCREAMING_SNAKE_CASE_  :	Optional[Any]			)       ->	None:





				"""simple docstring"""


				warnings.warn(
				    '''The class MobileViTFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'''
				    ''' Please use MobileViTImageProcessor instead.'''      ,SCREAMING_SNAKE_CASE_      ,)
				super().__init__(*SCREAMING_SNAKE_CASE_      ,**SCREAMING_SNAKE_CASE_			)


